Update app.py

app.py

@@ -1,52 +1,52 @@
-import streamlit as st
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-
-# Create a class for the session state
-class SessionState:
-    def __init__(self):
-        self.conversation_history = []
-
-# Initialize the session state
-session_state = SessionState()
-
-# Sidebar for setting parameters
-st.sidebar.title("Model Parameters")
-# You can add more parameters here as needed
-max_length = st.sidebar.slider("Max Length", 10, 100, 50)
-temperature = st.sidebar.slider("Temperature", 0.0, 1.0, 0.7)
-
-# Load the model and tokenizer with a loading message
-with st.spinner('Wait for it... the model is loading'):
-    model_name = "facebook/blenderbot-400M-distill"
-    model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
-    tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-# Create a chat input for the user
-input_text = st.chat_input("Enter your message:")
-
-# Check if the user has entered a message
-if input_text:
-    # Add the user's message to the conversation history
-    session_state.conversation_history.append(("User", input_text))
-
-    # Create conversation history string
-    history_string = "\n".join(message for role, message in session_state.conversation_history)
-
-    # Tokenize the input text and history
-    inputs = tokenizer.encode_plus(history_string, input_text, return_tensors="pt")
-
-    # Generate the response from the model with additional parameters
-    outputs = model.generate(**inputs, max_length=max_length, do_sample=True ,temperature=temperature)
-
-    # Decode the response
-    response = tokenizer.decode(outputs[0], skip_special_tokens=True).strip()
-
-    # Add the model's response to the conversation history
-    session_state.conversation_history.append(("Assistant", response))
-
-    # Display the conversation history using st.chat
-    for role, message in session_state.conversation_history:
-        if role == "User":
-            st.chat(message, is_user=True)
-        else:
-            st.chat(message, is_user=False)
+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+# Create a class for the session state
+class SessionState:
+    def __init__(self):
+        self.conversation_history = []
+
+# Initialize the session state
+session_state = SessionState()
+
+# Sidebar for setting parameters
+st.sidebar.title("Model Parameters")
+# You can add more parameters here as needed
+max_length = st.sidebar.slider("Max Length", 10, 100, 50)
+temperature = st.sidebar.slider("Temperature", 0.0, 1.0, 0.7)
+
+# Load the model and tokenizer with a loading message
+with st.spinner('Wait for it... the model is loading'):
+    model_name = "facebook/blenderbot-400M-distill"
+    model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Create a chat input for the user
+input_text = st.chat_input("Enter your message:")
+
+# Check if the user has entered a message
+if input_text:
+    # Add the user's message to the conversation history
+    session_state.conversation_history.append(("User", input_text))
+
+    # Create conversation history string
+    history_string = "\n".join(message for role, message in session_state.conversation_history)
+
+    # Tokenize the input text and history
+    inputs = tokenizer.encode_plus(history_string, input_text, return_tensors="pt")
+
+    # Generate the response from the model with additional parameters
+    outputs = model.generate(**inputs, max_length=max_length, do_sample=True ,temperature=temperature)
+
+    # Decode the response
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True).strip()
+
+    # Add the model's response to the conversation history
+    session_state.conversation_history.append(("Assistant", response))
+
+    # Display the conversation history using st.chat
+    for role, message in session_state.conversation_history:
+        if role == "User":
+            st.chat_message(message, is_user=True)
+        else:
+            st.chat_message(message, is_user=False)